Abstract
Medical image analysis based on deep learning technology has recently attracted much attention. However, it is inappropriate to directly employ the methods that perform well in computer vision. For skin lesion images, the differences between various lesions may be relatively small, and the existing commonly used datasets are class-imbalanced. In this paper, we propose a new method with an augmented loss function that makes use of contrastive information and label information. The proposed method tries to enhance the intra-class similarity and inter-class dissimilarity in the learning procedure. We also apply oversampling on the original data to tackle the imbalance issue. Extensive experiments are conducted on the ISIC2018 and ISIC2019 datasets. The results have demonstrated that, in terms of F1-score and AUC, the proposed method has outperformed the compared methods.
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Acknowledgements
The authors would like to thank the anonymous referees for their constructive comments that help improve the manuscript. This research was supported by the National Nature Science Foundation of China (Grant No. 62076040), the Natural Science Foundation of Shanghai (Grant No. 22ZR1466700), and the Foundation of Shanghai University of Medicine & Health Sciences (Grant No. A1-2601-23-311007-25, E1-0200-23-201009-17).
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Hu, S., Zhang, Z., Yang, J. (2023). Handling Intra-class Dissimilarity and Inter-class Similarity for Imbalanced Skin Lesion Image Classification. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_39
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